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Monitoring and predicting appliance emission using Machine Learning Approaches

  • Unique Paper ID: 180611
  • Volume: 12
  • Issue: 1
  • PageNo: 1583-1592
  • Abstract:
  • With the growing concerns about environmental pollution and energy efficiency, monitoring and predicting appliance emissions have become critical in ensuring sustainable resource utilization. Traditional emission tracking methods rely on periodic assessments and manual monitoring, which are often inefficient, time-consuming, and lack real-time adaptability. To address these challenges, this study explores the potential of machine learning (ML) approaches to enhance the accuracy and efficiency of appliance emission monitoring and prediction. The proposed system leverages data from sensors and appliance usage records to analyze emission patterns. Various machine learning models, including regression techniques, decision trees, and deep learning frameworks, are implemented to predict future emission levels. Feature engineering and data preprocessing techniques are employed to improve model accuracy. Performance evaluation is conducted using key metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R²) scores to compare the effectiveness of different models. Experimental results demonstrate that machine learning-based approaches significantly outperform conventional methods in predicting emissions with higher accuracy and adaptability. The study also highlights the challenges associated with data inconsistencies, sensor calibration, and real-time processing. The findings contribute to the development of intelligent, automated, and data-driven solutions for emission control, aiding policymakers, industries, and researchers in reducing environmental impact and promoting sustainable energy practices.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 1583-1592

Monitoring and predicting appliance emission using Machine Learning Approaches

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8.01 (Year 2024)
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Journal no 47859

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